Korea, Republic of

Summarizing Interactive Digital Narratives (IDN) presents some unique challenges to existing text summarization models especially around capturing interactive elements in addition to important plot points. In this paper we describe the first IDN dataset (IDN-Sum) designed specifically for training and testing IDN text summarization algorithms. Our dataset is generated using random playthroughs of 8 IDN episodes, taken from 2 different IDN games, and consists of 10,000 documents. Playthrough documents are annotated through automatic alignment with fan-sourced summaries using a commonly used alignment algorithm. We also report and discuss results from experiments applying common baseline extractive text summarization algorithms to this dataset. Qualitative analysis of the results reveal shortcomings in common annotation approaches and evaluation methods when applied to narrative and interactive narrative datasets. The dataset is released as open source for future researchers to train and test their own approaches for IDN text.

COLING 2022

IDN-Sum : A New Dataset for Interactive Digital Narrative Extractive Text Summarisation

interactive narratives

extractive summarisation

dataset

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

workshop paper

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### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

To gain access to this event page you are required to register. Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

The present paper describes the architecture of a novel Multi-Layer Long Text Summarizer (MLLTS) system proposed for the task of creative writing summarization. Typically, such writings are very long, often spanning over 100 pages. Summarizers available online are either not equipped enough to handle long texts, or
even if they are able to generate the summary, the quality is poor. The proposed MLLTS system handles the difficulty by splitting the text into several parts. Each part is then subjected to different existing summarizers. A multi-layer network is constructed by establishing linkages between the different parts. During training phases, several hyper-parameters are fine-tuned. The system achieved very good ROUGE scores on the test data supplied for the contest.


Summarization of Long Input Texts Using Multi-Layer Neural Network

We present the results of the Workshop on Automatic Summarization for Creative Writing 2022 Shared Task on summarization of chapters from novels. In this task, we finetune a pretrained transformer model for long documents called LongformerEncoderDecoder which supports seq2seq tasks for long inputs which can be up to 16k tokens in length. We use the Booksum dataset for longform narrative summarization for training and validation, which maps chapters from novels, plays and stories to highly abstractive human written summaries. We use a summary of summaries approach to generate the final summaries for the blind test set, in which we recursively divide the text into paragraphs, summarize them, concatenate all resultant summaries and repeat this process until either a specified summary length is reached or there is no significant change in summary length in consecutive iterations. Our best model achieves a ROUGE-1 F-1 score of 29.75, a ROUGE-2 F-1 score of 7.89 and a BERT F- 1 score of 54.10 on the shared task blind test dataset.

LED Finteuning and Recursive Summary Generation for Automatic Summarization of Chapters from Novels

Challenges and solutions for summarization of book chapters and dialogues

Where did I read that? Challenges in Summarization of Book Chapters and Dialogues

The Creative Summarization Shared Task at COLING 2022 aspires to generate summaries given long-form texts from creative writing. This paper presents the system architecture and the results of our participation in the Scriptbase track that focuses on generating movie plots given movie scripts. The core innovation in our model employs a two-stage hierarchical architecture for movie script summarization. In the first stage, a heuristic extraction method is applied to extract actions and essential dialogues, which reduces the average length of input movie scripts by 66\% from about 24K to 8K tokens. In the second stage, a state-of-the-art encoder-decoder model, Longformer-Encoder-Decoder (LED), is trained with effective fine-tuning methods, \emph{BitFit} and \emph{NoisyTune}. Evaluations on the unseen test set indicate that our system outperforms both zero-shot LED baselines as well as other participants on various automatic metrics and ranks 1st in the Scriptbase track.

Two-Stage Movie Scripts Summarization: An Efficient Method For Low-resource Long Documents Summarization

This presentation is addressing the problem of prime-time television screenplay summarization based on the SummScreen Forever Dreaming dataset. Our approach utilizes extended Longformers combined with sketch supervision including categories specifically for scene descriptions. Our system was able to produce the shortest summaries out of all submissions. While some problems with factual consistency still remain, the system was scoring highest among competitors in the ROUGE and BERTScore evaluation categories.

Long Input Dialogue Summarization with Sketch Supervision for Summarization of Primetime Television Transcripts

This presentation will discuss our shared task paper, which introduces a generalized AMR scheme to better model the long conversations in TV episodes. We hope to expose the diverse relations between concepts and complex dialogue structures so that they will be more accessible to transformer encoders. We will also discuss how the high-level information in a scene of a TV episode may guide the encoding of local tokens and utterances.

AMR-augmented Hierarchical Network for TV TranscriptSummarization

How to efficiently turn long documents into summaries with explorable structure? This has become an increasingly compelling question, given the rapid growth of document amount, length, and complexity. Existing summarization systems face new challenges for long documents: (1) Most systems only produce a generic paragraph-level summary, and cannot include all important points in a long document. (2) State-of-the-art summarizers rely on Transformer, whose time complexity is quadratic to the document length, and scales to hundreds of words at best.

In this talk, I first discuss the design of efficient attentions for long document summarization to handle more than 10k tokens while prior work only processes hundreds of words. I then present a new task: hierarchical question-summary generation, where we summarize the salient content in the source document into a hierarchy of questions and summaries, where each follow-up question inquires about the content of its parent question-summary pair. Learnable hierarchical biases are designed as part of the Transformer attention calculation, to adjust attention weights based on tokens’ relative positions with regard to the document structure. For both work, I will introduce new datasets to support experiments and discuss evaluation challenges.

Long Document Summarization using Efficient Attentions and Document Structure

Figurative language uses words in a way that deviates from their conventionally accepted definitions in order to convey a more complicated meaning or heightened effect. Figurative language generation can be very useful in many downstream tasks including fiction writing, essay writing, dialogue systems, etc. In this talk, I will first give an overview of figurative language generation and then introduce our recent work on a special case of figurative language generation, that is, hyperbole generation. We construct the first large-scale hyperbole corpus HYPO-XL and propose MOVER, an unsupervised approach to hyperbole generation.

Towards Figurative Language Generation

In this talk, we focus on video-to-text summarization and
investigate how to best utilize multimodal information for
summarizing long inputs (e.g., an hour-long TV show) into long
outputs (e.g.,~a multi-sentence summary). We extend
SummScreen (Chen et al., 2021), a dialogue summarization
dataset consisting of transcripts of TV episodes with reference
summaries, and create a multimodal variant by collecting
corresponding full-length videos. We incorporate multimodal
information into a pre-trained textual summarizer efficiently using
adapter modules augmented with a hierarchical structure while tuning
only~5\% of model parameters. Our experiments demonstrate that
multimodal information offers superior performance over more
memory-heavy and fully fine-tuned textual summarization methods.

Hierarchical3D Adapters for Long Video-to-text Summarization

This system description paper details TEAM UFAL’s approach for the SummScreen, TVMegasite subtask of the CreativeSumm shared task. The subtask deals with creat- ing summaries for dialogues from TV Soap operas. We utilized BART based pre-trained model fine-tuned on SamSum dialouge sum- marization dataset. Few examples from Au- toMin dataset and the dataset provided by the organizers were also inserted into the data as a few-shot learning objective. The additional data was manually broken into chunks based on different boundaries in summary and the di- alogue file. For inference we choose a similar strategy as the top-performing team at AutoMin 2021, where the data is split into chunks, either on [SCENE_CHANGE] or exceeding a pre- defined token length, to accommodate the max- imum token possible in the pre-trained model for one example. We implemented two differ- ent strategies as splits on [SCENE_CHANGE] did not necessarily mean having less than 1024 tokens in a segment.

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Summarization of Long Input Texts Using Multi-Layer Neural Network

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